关键词： Tactile Internet   mobile edge computing   haptric feedback   evolved packet system   data forwarding element   software defined network   network function virtualization  

摘要： In today’s communication era, we are speedily moving from various generations of mobile communication technologies to Next Generation mobile communication. Moving from 3G, 4G and 5G, one to the next generation, it includes in-built improvements in various characteristics. The world is collecting traditional information this is huge and providing the same to every corner of the world that creates huge pressure on the core networking and backhaul resources. This paper focuses on combining 5G technologies with mobile edge computing for robotic telesurgery. A huge amount of data is required to continuously transfer over the high-speed network for this process. This process demands high network bandwidth, minimal information loss, minimal delay and real-time response for precise surgery. As of today, useful and cost-effective solutions for medical domain telerobotic surgeries are not preferred yet may be due to the performance limitations of existing communication technologies like 4G. Hence, realtime medical video transmission using 5G enabled tactile (T5ET) internet technology environment is experimented in this research, with a focus on QoS parameters like jitter control, throughput, and delay. Promising results are observed in the evaluation and discussed here which will prominently contribute to the medical domain.

关键词： Eavesdropping   Jamming   Security   Unmanned aerial vehicles   Task analysis   Edge computing   Base stations   Physical layer security   mobile edge computing   UAV communication   secrecy capacity  

摘要： In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

关键词： NOMA   Power system reliability   Probability   Resource management   Task analysis   Uplink   Edge computing   Mobile edge computing   non-orthogonal multiple access   physical layer security   secrecy outage probability   partial offloading  

摘要： Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.

关键词： Task analysis   Wireless communication   Sensors   Resource management   Wireless sensor networks   Servers   Energy consumption   Mobile edge computing   binary offloading   optimization algorithms   resource allocation  

摘要： Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size.

关键词： Internet of Things (IoT)   joint communication and computation resource allocations   mobile edge computing   non-orthogonal multiple access  

摘要： The past decades have witnessed an explosive growth of the Internet of Things (IoT) services requiring intensive computation resources. The conventional IoT devices, however, are usually equipped with very limited computation resources, which results in degraded quality of experience when executing the resource-hungry applications. Mobile edge computing (MEC), which enables smart terminals (STs) to offload parts of their computation workloads to the edge servers located at cellular base stations (BSs), has provided a promising approach to address this issue. In this article, we investigate the nonorthogonal multiple access (NOMA)-enabled multiaccess MEC. Specifically, by exploiting the advanced NOMA, an ST can simultaneously offload its computation workloads to different edge servers (ESs), which thus reduces the overall delay in completing the ST's computation workloads. To study this problem, we formulate a joint optimization of the computation resource allocations at the ESs, the ST's offloaded workloads and its radio resource allocations for NOMA transmission, with the objective of minimizing a system wise cost that accounts for the overall delay in finishing the ST's total computation workload and the total computation resource usage cost at the ESs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient layered algorithm to find the optimal solution. By exploiting the optimal offloading solution of a single ST, we further investigate the scenario of multiple STs and propose two algorithms to determine the optimal grouping among different ESs for serving the STs, with one algorithm aiming at minimizing the total cost of all STs and the other algorithm aiming at determining the Nash stable grouping for the ESs. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed NOMA-enabled multiaccess computation offloading.

关键词： Mobile edge computing   sequential task offloading   ultra-reliable low-latency task offloading  

摘要： In this letter, a collision-free sequential task offloading scheme to multiple mobile-edge computing servers is proposed. The problem is formulated as a multi-objective optimization of latency and offloading failure probability. An exact solution technique is developed to obtain a benchmark optimal solution for the problem. A more computationally efficient heuristic algorithm is additionally developed, whose sub-optimal solution yields a performance close to optimal. Simulation results illustrate that the proposed offloading scheme can effectively reduce both latency and offloading failure probability.

关键词： End-edge-cloud orchestration   network computing   transparent computing   mobile edge computing   fog computing   cloudlet  

摘要： Sending data to the cloud for analysiswas a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

关键词： Mobile social networks   mobile edge computing   caching   deep reinforcement learning  

摘要： Social networks have continuously been expanding and trying to be innovative. The recent advances of computing, caching, and communication (3C) can have significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share resources (e.g., information, computation-based services). In this paper, we exploit the intrinsic nature of social networks, i.e., the trust formed through social relationships among users, to enable users to share resources under the framework of 3C. Specifically, we consider the mobile edge computing (MEC), in-network caching and device-to-device (D2D) communications. When considering the trust-based MSNs with MEC, caching and D2D, we apply a novel deep reinforcement learning approach to automatically make a decision for optimally allocating the network resources. The decision is made purely through observing the network's states, rather than any handcrafted or explicit control rules, which makes it adaptive to variable network conditions. Google TensorFlow is used to implement the proposed deep Q-learning approach. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

关键词： Vehicular model   Mobile edge computing   Vehicular service cloud   Vehicular cloud computing   5G  

摘要： With 5G network services around the corner, vehicular cloud networks providing computation capabilities have taken precedence over traditional costly cloud solutions. However, with vehicular cloud computing, a variety of new challenges have grown. In this paper we propose an infra-vehicle resource sharing model to provide a range of cloud services such as on-demand entertainment and speech recognition for driver assistance. The proposed solution forms nearby low-latency Vehicular Service Clouds (VSC) on-the-fly as per the needs of vehicular users. Vehicles in parking lots or moving on the road collaborate and share their computation and storage resources to complete different vehicular service requests. We develop an incentive-based model that uses edge-based Road Side Units (RSU) to compose heterogeneous node resources and produce a usable resource that satisfies users' requests with minimal delays. Through proof of concept simulations, we compare our solution against traditional cloud solutions to showcase the effectiveness of adopting our proposed framework.

关键词： Cognitive agent   Mobile edge computing   Caching strategy   Resource collaboration  

摘要： The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users' behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users' equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users. (C) 2019 Elsevier B.V. All rights reserved.